SOTAVerified

Semantic Similarity

The main objective Semantic Similarity is to measure the distance between the semantic meanings of a pair of words, phrases, sentences, or documents. For example, the word “car” is more similar to “bus” than it is to “cat”. The two main approaches to measuring Semantic Similarity are knowledge-based approaches and corpus-based, distributional methods.

Source: Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection

Papers

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Showing 676–700 of 1564 papers

TitleStatusHype
Efficient Audio Captioning Transformer with Patchout and Text Guidance—0
SuperDisco: Super-Class Discovery Improves Visual Recognition for the Long-Tail—0
Using Semantic Similarity and Text Embedding to Measure the Social Media Echo of Strategic Communications—0
LEURN: Learning Explainable Univariate Rules with Neural Networks—0
A Novel Patent Similarity Measurement Methodology: Semantic Distance and Technological DistanceCode0
Micro-video Tagging via Jointly Modeling Social Influence and Tag RelationCode0
INO at Factify 2: Structure Coherence based Multi-Modal Fact VerificationCode0
AdapterSoup: Weight Averaging to Improve Generalization of Pretrained Language Models—0
NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching SummarizationCode0
A Parametric Similarity Method: Comparative Experiments based on Semantically Annotated Large Datasets—0
Analyzing the impact of climate change on critical infrastructure from the scientific literature: A weakly supervised NLP approach—0
How to choose "Good" Samples for Text Data Augmentation—0
TransFool: An Adversarial Attack against Neural Machine Translation ModelsCode0
KNNs of Semantic Encodings for Rating Prediction—0
uHelp: intelligent volunteer search for mutual help communities—0
ClusterLog: Clustering Logs for Effective Log-based Anomaly Detection—0
Syntactically Robust Training on Partially-Observed Data for Open Information ExtractionCode0
Prompting Large Language Model for Machine Translation: A Case Study—0
USER: Unified Semantic Enhancement with Momentum Contrast for Image-Text RetrievalCode0
Language-Informed Transfer Learning for Embodied Household Activities—0
Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss—0
Universal Multimodal Representation for Language Understanding—0
The Undesirable Dependence on Frequency of Gender Bias Metrics Based on Word EmbeddingsCode0
Scene-Aware Label Graph Learning for Multi-Label Image Classification—0
Text-Guided Unsupervised Latent Transformation for Multi-Attribute Image Manipulation—0
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All datasetsAnnotated corpus for semantic similarity of clinical trial outcomes (expanded corpus)Annotated corpus for semantic similarity of clinical trial outcomes (original corpus)SICKBIOSSESCHIP-STSClinicalSTSMedSTS

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BioBERT (pre-trained on PubMed abstracts + PMC, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")F193.38—Unverified
2SciBERT uncased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")F191.51—Unverified
3SciBERT cased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")F190.69—Unverified
4BERT-Base uncased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")F189.16—Unverified
5BERT-Base cased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")F189.12—Unverified
#ModelMetricClaimedVerifiedStatus
1BioBERT (pre-trained on PubMed abstracts + PMC, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")F189.75—Unverified
2SciBERT uncased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")F189.3—Unverified
3SciBERT cased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")F189.3—Unverified
4BERT-Base uncased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")F186.8—Unverified
5BERT-Base cased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")F184.21—Unverified
#ModelMetricClaimedVerifiedStatus
1Doc2VecCMSE0.31—Unverified
2LSTM (Tai et al., 2015)MSE0.28—Unverified
3Bidirectional LSTM (Tai et al., 2015)MSE0.27—Unverified
4combine-skip (Kiros et al., 2015)MSE0.27—Unverified
5Dependency Tree-LSTM (Tai et al., 2015)MSE0.25—Unverified
#ModelMetricClaimedVerifiedStatus
1BioLinkBERT (large)Pearson Correlation0.94—Unverified
2BioLinkBERT (base)Pearson Correlation0.93—Unverified
3NCBI_BERT(base) (P+M)Pearson Correlation0.92—Unverified
#ModelMetricClaimedVerifiedStatus
1MacBERT-largeMacro F185.6—Unverified
#ModelMetricClaimedVerifiedStatus
1CharacterBERT (base, medical, ensemble)Pearson Correlation85.62—Unverified
#ModelMetricClaimedVerifiedStatus
1NCBI_BERT(base) (P+M)Pearson Correlation0.85—Unverified